code: def multi_run(config):
      config["save_model_path"] = "{}_{}_{}_{}".format(
          config["save_model_path"],
          config["dataset"],
          "gcn",
          config["direction_option"],
      )
      print_config(config)
  
      logger = Logger(
          config["save_model_path"], config=config, overwrite=True
      )
  
      config = dict_to_namedtuple(config)
      np.random.seed(config.random_seed)
      scores = []
      for _ in range(config.num_runs):
          seed = np.random.randint(10000)
          scores.append(main(config, seed))
  
      print(
          "\nTest Accuracy ({} runs): mean {:.4f}, std {:.4f}".format(
              config.num_runs, np.mean(scores), np.std(scores)
          )
      )
      logger.write(
          "\nTest Accuracy ({} runs): mean {:.4f}, std {:.4f}".format(
              config.num_runs, np.mean(scores), np.std(scores)
          )
      )
      logger.close()
  
imports: import argparse
  import os
  import time
  from collections import namedtuple
  import dgl
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from ...data.data import GraphData
  from ...modules.graph_embedding_learning.gat import GAT
  from ...modules.utils.generic_utils import get_config, grid, print_config
  from .utils import EarlyStopping
label_desc: Use image node_id 6 for calling a global function with example usage: multi_run(config) without return types